ARTIFICIAL INTELLIGENCE IN HYBRID CLOUD SECURITY: ENHANCING THREAT DETECTION AND RESPONSE

Abstract

The rapid adoption of hybrid cloud environments has introduced new security challenges, necessitating advanced solutions for threat detection, incident response, and risk mitigation. Traditional security approaches struggle to keep pace with evolving cyber threats, prompting organizations to integrate Artificial Intelligence (AI) into their security frameworks. AI enhances hybrid cloud security by leveraging machine learning, deep learning, and predictive analytics to detect anomalies, automate responses, and improve overall threat intelligence. AI-driven intrusion detection systems (IDS), security orchestration, automation, and response (SOAR), and AI-enhanced Security Information and Event Management (SIEM) platforms significantly improve the speed and accuracy of identifying cyber threats in real-time. AI-powered endpoint security and behavioral analytics contribute to proactive threat prevention. AI adoption in cybersecurity is not without challenges, including adversarial attacks, bias in AI models, ethical concerns, and the need for substantial computational resources. This paper explores the role of AI in strengthening hybrid cloud security, comparing AI-based solutions with traditional approaches, and highlighting real-world implementations. The study also discusses the limitations and future trends in AI-driven security, including federated learning and blockchain-based security enhancements. This paper aims to provide insights into the evolving landscape of AI-powered hybrid cloud security and its potential for mitigating emerging cyber risks.